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Record W2101360293 · doi:10.1002/2013rs005354

Ionospheric tomography using ADS-B signals

2014· article· en· W2101360293 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRadio Science · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsTECTotal electron contentIonosphereTomographyAlgebraic Reconstruction TechniqueGeologyFaraday effectRay tracing (physics)Polarization (electrochemistry)Electron densityTransmitterRemote sensingPhysicsOpticsGeodesyComputer scienceGeophysicsElectronTelecommunications

Abstract

fetched live from OpenAlex

Numerical modeling has demonstrated that Automatic Dependent Surveillance Broadcast (ADS-B) signals can be used to reconstruct two-dimensional (2-D) electron density maps of the ionosphere using techniques for computerized tomography. Ray tracing techniques were used to determine the characteristics of individual waves, including the wave path and the state of polarization at the satellite receiver. The modeled Faraday rotation was computed and converted to total electron content (TEC) along the raypaths. The resulting TEC was used as input for computerized ionospheric tomography (CIT) using algebraic reconstruction technique. This study concentrated on reconstructing mesoscale structures 25–100 km in horizontal extent. The primary scientific interest of this study was to show that ADS-B signals can be used as a new source of data for CIT to image the ionosphere and to obtain a better understanding of magneto-ionic wave propagation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.230
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it